Introduction

This page provides up-to-date information about using the SPIRE instrument: from preparing observations to reducing your data. This page also provides you with the latest calibration accuracies and known SPIRE calibration issues.

We also provide access to the latest stable developer build (latest stable CIB).

BewareThese developer builds do not undergo the same in-depth testing as the user releases do. The latest developer build can be found here. Please contact the Herschel helpdesk if you plan to use a developer build as there may be some additional information needed in order for you to properly make use of it.

Within HIPE you can access all the SPIRE data reduction and HIPE-user documentation. The SPIRE Data Reduction Guide (SDRG) follows the user pipeline scripts and also explains the details of pipeline processing and data analysis. It is also available online here:

SPIA: The SPIRE Photometer Interactive Analysis (SPIA) package is available as a plug-in for HIPE. SPIA provides a structured GUI based access to the more intricate parts of the scan map photometer pipeline for SPIRE without the immediate need to resort to scripts. More information can be found in the SDRG or on the SPIA web page

The SPIRE Launch Pads

The SPIRE Launch Pads are single sheet quick entries (like a cheat sheet) into SPIRE data reduction and providing quick references to the relevant sections in the SPIRE Data Reduction Guide. There are launch pads for Data Access, SPIRE Photometer and Spectrometer data reduction.

Spectrometer data reduction

The best source of information for reducing SPIRE Spectrometer data is the SPIRE Data Reduction Guide available through the HIPE help. This runs through the User Pipeline scripts step by step, describes several other Useful Scripts, and offers advice for specific types of sources:

Faint (<10 Jy) and medium (<100 Jy) strength sources

Bright sources (>500 Jy)

Semi-extended sources

Spectral mapping observations

Observations with few repetitions

For faint sources, the subtraction of instrument, telescope and background emission is particularly important. Optimum subtraction can be performed in several ways (read the SPIRE Data Reduction Guide for details):

Photometer data reduction

Overview

The best source of information for reducing SPIRE Photometer data is the SPIRE Data Reduction Guide available through the HIPE help. This runs through the User Pipeline scripts step by step, describes several other Useful Scripts, and offers advice for specific issues that might be encountered.

Data Processing Issues

The main issues that you might find in your data are: undetected glitches, thermistor or detector jumps, and bad baseline removal.

Stripes in PSW, PMW and/or PLW (Level 2) maps

All SPIRE Photometer pipelines now use the destriper by default, which improves the issue of stripes in Level 2 maps. There should be noticeable improvements in that respect from HIPE version 9 onwards. The destriper documentation can be found on the NHSC website

De-glitcher masks faint sources

For data taken in Parallel Mode in particular (sampling at 10Hz, at high speed 60"/s), the de-glitcher may flag very faint sources as glitches when it is run with standard parameters. Faint sources may have a "delta function" shape due to the low sampling rate, which looks similar to a small glitch. Try modifying the "correlation parameter" to 0.95: this will decrease the number of detected glitches - it may be better to have a limited detection rate in first level deglitching and defer to Level 2 deglitching.

Cooler temperature variations (Cooler Burps)

The SPIRE cooler is recycled after 48 hours. Between 6 to 7h after the cooler recycle ends, its temperature rises steeply and reaches the stable plateau. Observations taken during such times may exhibit stripes in the final maps. An option to correct for this effect is now available in the User Pipelines (See the SPIRE DRG for details).

NaN pixels present in the PSW, PMW and/or PLW (Level 2) maps

This effect, related to data masking or poor coverage, is more evident in single fast-scan Parallel Mode maps. To avoid NaNs, increase the pixel size (i.e., decrease the map's resolution).

This effect can also occur with destriped maps. In this case check if increasing the sigma parameter or switching off the Level 2 deglitcher helps.

Quality flags in the quality context

Currently, the quality flags at the quality context inside the observation context are just meant for HSC/ICC internal evaluation of the quality of the products and not for the users. In case the data had some serious quality problem, the PI of the program has been contacted about it. Otherwise, only information in the quality summary, when available, should concern the observers.

Known Issues in ODs 1304 & 1305

For (yet) unknown reasons, the three detectors PSW-B5, PSW-E9 and PSW-F8 - that use to behave well during the entire mission - were noisy during the two operational days 1304 and 1305. The result are stripes visible in the final PSW map which the current (HIPE 11) pipeline is not able to correct. The solution is to mask and exclude these detectors from the analysis. This could be done in 2 ways:

You can use the SpireMaskEditor GUI as described in Sec. 8.4 of the SPIRE Data Reduction Guide: write-click on your observation context variable and then select Level1_SpireMaskEditor and set to Master all samples in all scans (listed as BBID) for the detectors mentioned above.

After either of those cases, you must then re-run level 1 to 2 steps on the newly modified level1 product. If your observation has been already re-reduced with HIPE 11, original and new level1s are already destriped, so you can directly run the naive map-maker on the new level1. Otherwise, you must run the destriper step: check the pipeline script for details.

As of HCSS 11, a new task named zeroPointCorrection is available to the users: this task calculates the absolute offset for a SPIRE map based on cross-calibration with HFI-545 and HFI-857 maps, colour-correcting HFI to SPIRE wavebands assuming a grey body function with fixed beta.

Source Extraction and Photometry

The current recommended method for photometry sourceExtractorTimeline task (formerly known as the Timeline Fitter) which works on the detector timelines. The Map based algorithm sourceExtractorSussex (SUSSEXtractor) providers good results and is useful on larger maps where the sourceExtractorTimeline will be significantly slower. sourceExtractorDaophot (DAOphot) also provides a reasonable estimate of the source flux but may require an aperture correction.

Photometry on single direction fast scan parallel mode maps: The photometry on single scan direction fast parallel mode results in higher photometric errors of up to 5 percent for aperture photometry compared to nominal speed and cross linked maps. The best results are obtained using the Timeline Fitter. Wherever possible orthogonal and nominal direction parallel scans should be merged.

SPIRE report from the January 2013 HSC Map Making Workshop

The official release of the report of SPIRE map-making test campaign (2013) can be downloaded as a PDF.

Cookbooks

The standalone "Photometry Cookbook", is no longer maintained - it is being incorporated into the SPIRE DRG - please see the SDRG for photometry cookbook information, and raise a Helpdesk ticket if you find something missing.

SPIRE calibration file versions

The available calibration trees for SPIRE are listed below (with the current operational version at the top).

Any of the calibration trees can be retrieved in HIPE from the HSA using (e.g.) cal = spireCal(calTree="spire_cal_12_2") etc. The default (applicable to the HIPE version in use) can be obtained with cal = spireCal(calTree="spire_cal"). It can then be saved to a local pool right-clicking on the cal variable and then selecting from the context menu Send To -> Local Pool.

Alternatively, the latest calibration tree for SPIRE can be obtained as a jar file from Latest calibration trees. Then, you have to possibilities to read and save:

The jar file can be load directly into HIPE with the command: cal = spireCal(jarFile="PATH_TO_FILE/spire_cal_12_2.jar"). To save it to a local pool, proceed as described above, right-clicking on the cal variable and then selecting from the context menu Send To -> Local Pool.

The jar file can also be saved directly to a local pool without opening HIPE, running the following command in the terminal command line: cal_import PATH_TO_FILE/spire_cal_12_2.jar. Then, to load the calibration tree in HIPE, simply type: cal = spireCal(pool="spire_cal_12_2")

SPIRE calibration and performance

Photometer calibration

SPIRE Photometer Calibration:Full details of the SPIRE calibration can be found in the SPIRE Handbook and in dedicated publications: the calibration scheme is described in Griffin et al. (2013) and the implementation using Neptune as the primary calibration standard, is described in Bendo et al. (2013).

Calibration uncertainties, which should be included in addition to the statistical errors of any measurement, are as follows:

± 4% absolute from Neptune model (this uncertainty is systematic and correlated across the three bands)

± 1.5% (random) from Neptune photometry

Extended emission calibration

In addition to the above uncertainties, there is an additional ±4% uncertainty due to the current uncertainty in the measured beam area

SPIRE Photometer Beams:

These are available in the SPIRE calibration context, at the standard map pixel size of (6,10,14) arcsec/pixel for (250,350,500) µm bands, and can be accessed in HIPE after a calibration context has been loaded (see above).

A new more detailed analysis of the SPIRE beam profile data was undertaken in 2012, leading to revised values for beam profile solid angles and derivation of a semi empirical wavelength dependent beam profile model. The results at a scale of 1 arcsec/pixel as well as the data needed for the model are available for download. A detailed description of the analysis is given as well.

SPIRE Photometer filter transmission curves:

These are also available in the SPIRE calibration context (photRsrf) and can be accessed in HIPE after a calibration context has been loaded (See above).

Neptune and Uranus models used for the SPIRE photometer flux calibration:

The ESA2 models used up to HIPE v10 and spire_cal_10_1, are available here.

The ESA4 models used from HIPE v11 and spire_cal_11_0, are available here.

Spectrometer calibration

Calibration uncertainties, which should be included in addition to the statistical errors of any measurement from HIPE v11 onwards, are as follows:

Point sources observed on the centre detectors (SSWD4 and SLWC3): the measured repeatability is 6%, with the following contributions: (i) absolute systematic uncertainty in the models from comparison of Uranus and Neptune - determined to be ±3%; (i) the statistical repeatability determined from observations of Uranus and Neptune, with pointing corrected - estimated at ±1% (excluding the edges of the bands); (iii) the uncertainties in the instrument and telescope model, which lead to an additive continuum offset error of 0.4 Jy for SLW and 0.3 Jy for SSW and (iv) the effect of the Herschel APE.

Sparse observations of significantly extended sources:

the absolute uncertainty in intensity for a reasonably bright, fully extended object, observed in the central detectors is, in theory, ±1%, with the following contributions: (i) the systematic uncertainty in telescope model of 0.06%; (ii) the statistical repeatability estimated at ±1% and (iii) an additive continuum offset of 3.4x10-20 W/m2/Hz/sr for SLW and 1.1x10-19 W/m2/Hz/sr for SSW.

In practice, truly extended sources tend to be faint and the uncertainty is therefore dominated by the additive offsets. When the source extent is larger than the main beam size, but not fully extended, or if there is structure inside the beam, then the uncertainties are dominated by the source-beam coupling (see Wu et al. 2013 ) and are significantly greater than 1%.

Mapping mode: the variations between detectors becomes important and the overall repeatability has been measured as ±7% (see Benielli et al. 2013, submitted, for a full discussion of mapping mode observations). The off-axis detectors are less well calibrated, especially outside the unvignetted part of the field.